BSGW (0.9.2)

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Bayesian Survival Model with Lasso Shrinkage Using Generalized Weibull Regression.

http://cran.r-project.org/web/packages/BSGW

Bayesian survival model using Weibull regression on both scale and shape parameters. Dependence of shape parameter on covariates permits deviation from proportional-hazard assumption, leading to dynamic - i.e. non-constant with time - hazard ratios between subjects. Bayesian Lasso shrinkage in the form of two Laplace priors - one for scale and one for shape coefficients - allows for many covariates to be included. Cross-validation helper functions can be used to tune the shrinkage parameters. Monte Carlo Markov Chain (MCMC) sampling using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package MfUSampler) is used for coefficient estimation.

Maintainer: Alireza Mahani
Author(s): Alireza S. Mahani, Mansour T.A. Sharabiani

License: GPL (>= 2)

Uses: doParallel, foreach, MfUSampler, survival
Reverse suggests: CFC

Released about 2 years ago.


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